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PINBOARD SUMMARY

Membrane proteins play a pivotal role in biology, representing a quarter of all proteomes and a majority of drug targets. While considerable effort has been focused on improving our functional understanding of this class, much of the investment has been hampered by the inability to obtain sufficient amounts of sample. Until now, there have been no broadly successful strategies for predicting and improving expression which means that each target requires an ad hoc adventure. Complex biological processes govern membrane protein expression; therefore, sequence characteristics that influence protein biogenesis are not simply additive. Many properties must be considered simultaneously in predicting the expression level of a protein.

We provide a first solution to the membrane protein expression problem by learning from published data to develop a statistical model that predicts the outcomes of expression trials across families, scales, and laboratories (all independent of the model’s training data). Given that the process of finding a target for large-scale expression is arduous, often requiring a long trial-and-error process that consumes significant financial and human resources, this work will have immediate applicability. The ability to study and engineer inaccessible membrane proteins becomes feasible with the use of our predictor. Furthermore, this work will enable others in developing new computational methods to assist in the experimental study of membrane proteins.